Brighcove is an online video platform that allows to embed onDemand videos on your websites. It seems there’re out of there some people looking for a way to track it, so we’re going to learn how to track those videos within Google Tag Manager.

In order to be able to track our videos from BrightCove we need to have some things in mind:

API needs to be enabled within BrightCove Interface.

Embeds needs to have includeAPI and templateLoadHandler params in place.

First Step: Enabling the JS API for our videos

This needs to be done within BrightCove Site for all the different players that we may have, follow these steps:

Go to your Video Cloud Studio Publishing Module and then select your player

Second Step: GTM Configuration

For being able to track BrightCove videos, we’re going to use 1 Custom HTML tag, 1 Trigger and 1 Variable.
First let’s create the Variable, this one will allow us to know when a BrightCove Video is present on a page, to we just fire the tracking script when it’s needed. We don’t want to inject the tracking code were is not going to be used:
Now, we’re going to create the Trigger that is going to fire our tracking code. Just to be safe, we’re going to fire it on DomReady and when our previusly created Variable equals to “yes”.
Ok, now let’s add the tracking tag (you’ll be able to copy the js code from the post bottom):
We’re done. Remember to test this code in preview before going live.
This script will track the following events:

Play

Pause

Complete

Progress (25% steps, it can be personalized within the code)

Seek

We’ve used an agnostic dataLayer push, with a lot of extra info about the video in the case you need it, like the related thumbnail, the video length, the video coded, the video name, the publisher info, check the following screenshot for a guide of all info you may use for your custom dimensions:

Metrics, Metrics, Metrics, oh my!!

With the recent (very welcome) release of calculated metrics in Google Analytics, I thought that the time was ripe to write a post about custom metrics in GA. Towards the end of this post you will see why I believe that custom metrics are quite a germane topic to discuss in relationship with calculated metrics. I will also touch on how the launch of calculated metrics is another big value prop when it comes to GA Premium (even though they aren’t a GAP only feature).

I believe that custom metrics are painfully underused in Google Analytics implementations. Believe it or not, my browser console is oftentimes open when I’m browsing the web (you should believe it). I do a lot of spying investigating of websites’ implementations by looking at the data that they send to GA , and I do not see custom metrics being deployed almost at all.

Dimensions and Metrics

Before we dive into custom metrics and calculated metrics, let’s take a step back and define “metrics”. There are a number of differentarticles which define dimensions and metrics. I have chosen to quote Paul Koks and then go on to discuss in my own words (recognizing that my own understanding of dimensions and metrics comes from reading those who precede me in the industry). In Paul’s words:

A dimension is a characteristic of an object that can be given different values —> a dimension describes data

A metric is an individual element of a dimension which can be measured as a sum or ratio —> a metric measures data

So, there we have it. Dimensions describe data and metrics measure data. Dimensions will make up the rows in a table report whereas metrics will populate the columns. Metrics are the data. Metrics increment, they count things. Metrics will invariably be a number, be it an integer, a ratio, a percentage etc… As of today (if I counted correctly), there are 189 metrics available in the Google Analytics API.

Custom Metrics & Event Tracking (Measuring User Interactions)

So here is where metrics begin to get interesting. Or, as my friend Jacques Warren once said to me, custom metrics are “basically where reports really start making sense to a business.” Standard metrics in Google Analytics are meaningless by themselves. This is mostly true of custom metrics as well. Remember, they are just counters, so when they are stand-alone they lack any context. Meaning must be derived through segmentation, and that means drilling down into the dimensions which describe your data. While custom metrics won’t solve the context problem for you completely, they do so partially, which is why I love them so much.

Let’s start by taking a look at how some standard metrics in Google Analytics are used to measure user behavior. I’m going to start with Event Tracking, using an ecommerce site as my example. Event tracking is used to track interactions with a website or app. Those interactions are usually some form of click, form submit, tap (for mobile), etc which translate into discreet actions that a user is taking.

In the above example, my Event Category (a dimension) is “Product Page”. It serves as a descriptor for the Event Action (which also a dimension), which describes the metric (total events). Notice how total events is simply a number. It counts how many times an action in rows 1-6 happened.

Now let’s say I want to get some more context about the Add to Basket event is being tracked. One way that I can get at this information is via Custom Reports. With a custom report, I can drill down into different dimensions as a way of getting more granular context in my data.

Similarly, by using the Pivot feature in reports will allow you to provide additional context to your metrics.

But in all of these examples, the metric itself, “total events”, still lacks context. In order to know “total events” of what, I need to refer to my dimensions.

Custom Metrics in Google Analytics are unique in the fact that they are “named values”; they can also describe the interaction that they are measuring. Just like custom dimensions, the name of the custom metric is set in the GA Admin section.

Notice how each of the actions that the user can take on the product page is now being tracked as a metric. This is really powerful as now I can build a custom report and pair these custom metrics with almost any dimension (limitations to be discussed below).

Another example is from a site that has has restaurant reviews. On an individual restaurant details the user is able to view a menu, check hours, add a calendar reminder about their reservation, or even order food online.

As you can see, custom metrics make the data model in Google Analytics much more flexible. As far as event tracking goes, I highly recommend taking 10 or 15 most meaningful events (user interactions) and tracking them with a custom metric as well. In my humble opinion, custom metrics can be seen as “event tracking on steroids” (almost the title of this post). Context gives meaning to data. These data points, since they are named, are now much more meaningful than just being a “total event”. While the pivot function in certain GA reports can get you kind of close to what you see in the table above, it is by far not a replacement for have a native metric in GA.

Tracking $$$

In addition to super charging your tracking of user interaction with your site, I find custom metrics to be wonderful when it comes to tracking financial data. My favorite custom metric in this regards is gross profit, though with some hard work tracking net profit is possible in Google Analytics as well (though that would like require some calculated metrics also).

In almost all of our ecommerce implementations, we try to track the following metrics when available.

Original Price / List Price

This is $119.99 for the item above

Displayed Price

This is $109.00 for the item above

Product Level Discount

This is product level savings, $10.99

Order Coupon Value per Product

If a 15% coupon is applied to the whole cart, that 15% is applied equally to every product. For our product above that would $16.35

Product Revenue

This is a standard metric (not custom), that represents the actual value of transacted revenue per product

Cost of Goods Sold

FWIW, I bet a bunch of folks are making a killing on that Lego set.

Gross Profit

Product Revenue minus COGS

The results are totally terrific. 🙂

The above image shows the breakdown of sales by brand (including how much was discounted at point of sale by brand). With the above custom metrics in place, I can quite clearly see the impact of discounting / sales / promotions etc on any product level dimension or on session and user level dimensions. In other words, I could run reports to see how much money was being left on the table by channel, by geography, by customer type, by gender, by age group. The list goes on and on.

Some Technical Points

WARNING: If you don’t like reading about technical aspects of Google Analytics and implementations, just skip to the next section.

For reasons I understand, but don’t particularly like, custom metrics are “scoped” (as are custom dimensions). In other words, they need to be set to a particular data scope in the Google Analytics admin in order to be processed into reports. Custom metrics support two scopes, Hit and Product. Metrics will be processed and then reported on as either integers, currency, or time (measured in seconds).

When hit level data is sent to Google Analytics, the parameter / value combination matches the following format: cmXX={{value}} where XX is the index of the custom metric. The actual name of the metric that you see in reports is configured in the admin, just as you would configure a custom dimension (as in the image earlier in this post). So let’s say I wanted to track an Add to Wishlist interaction with a custom metric, I would use the following code:
ga(‘send’, ‘event’, ‘Product Page’, ‘Add to Wishlist’, ‘Expensive Stars Wars Lego Set’, {‘metric2’: 1});

In Google Tag Manager, you can configure the event set a custom metric index and value directly in the event tag.

Unfortunately, GTM is using a “set” command for a custom metric, which means that the value will persist for the same tracking object. If you’re using a named tracker, you’re in trouble because all future hits will have that value set. Even though most people default to not using a named tracker, this still creates problem for custom metrics that are sent with pageviews, as page timing hits will use the same tracking object even when no default tracker name is chosen. It can get really nasty with single page sites (think angular.js) 🙁

Hashtag #booooo. (There are workarounds using hitcallbacks, but I still #boo the need to resort to those implementation acrobatics, when pure hit level manipulation in GTM should be available).

Product scoped custom metrics are coded as part of the product actionFieldObject. This makes perfect sense as the metric is meant to track a particular value associated with a particular action. For example, the value of the product added or removed from cart (a good article if you’re interested in a practical walkthrough of how product level custom metrics are implemented). Or any of the discount / profit metrics that I mentioned above for the “transaction” action (say that five times fast). Since multiple products can be associated with a particular hit, you can pass through the product level discount for each product on a “purchase” action that uses a pageview hit as the data transport mechanism.

The data is sent to GA as in a &prXXcmXX format, which stands for product {{number}} and cm {{number}}

Limitations of “product scope”

One particular thorn in my side when it comes implementing custom metrics for Enhanced Ecommerce implementations is the inability to track interactions with products outside of the predefined “dictionary” that Google supports when it comes to interactions with products. Those actions are (I’m quoting):

click

A click on a product or product link for one or more products.

detail

A view of product details.

add

Adding one or more products to a shopping cart.

remove

Remove one or more products from a shopping cart.

checkout

Initiating the checkout process for one or more products.

checkout_option

Sending the option value for a given checkout step.

purchase

The sale of one or more products.

refund

The refund of one or more products.

promo_click

A click on an internal promotion.

That means that Adding to Wish List, Adding to Registry, Viewing Product Video, Clicking on Cross-sell, Social Sharing, Writing a Review, Asking A Question, etc etc cannot be tracked on a product level in the same way that the other items are tracked. As of this post, changing the actionFieldObject to some use other name (so that you don’t inflate “adds” or “clicks”) and setting a product level custom metric to capture the interaction with the product will simply fail. This means that in order to track information about product interactions that are not part of the Enhanced Ecommerce dictionary, you’ll need to use hit level tracking (events). As such, collecting important data such as Wish List Adds per product or per brand request will require the use of “hit level” custom dimensions, not product level.

Calculated Metrics

So far we’ve seen how custom metrics are super useful, that they add a particular flexibility to the data you collect in GA (pivoted data galore), and how they are indeed critical for tracking the metrics that really matter (profit!). The reason that the release of calculated metrics was a catalyst for me to write about custom metrics is because in order create calculated metrics, you need to use metrics. “HUH?” Yes, that’s correct. I did just say that. Let me illustrate what I mean with a few examples.

My first example is from my friend Peter O’Neill’s post about using calculated metrics to measure conversion funnel completion rates.

Image ripped without written permission from Peter

To see the percentage of sessions where users viewed a product out of the total sessions where users viewed the ecommerce portion of the site (i.e. the store as opposed to the blog), you need two metrics, sessions with views of store and sessions with views of product page. The simplest way to get these two metrics is to create goals for each of these steps (something that you should be doing in any case). You then divide Goal X by Goal Y. Pretty straightforward, relatively benign. Create goals to get measures of number of sessions where a core action was taken

My second example is from my friend Charles Farina, who has a nice article with 25 calculated metric examples which includes some basic “how-to” in terms of setup. The example is “video completion rate.” He mentions, as an aside, that in order to create this calculated metric, you’ll need custom metrics. Why are custom metrics needed, you ask? Well, let’s look at how I would normally calculate video completion rate.

Note the numbers here. The number of sessions where the video was completely viewed was 20,451 times. That metric, 20,451, in Google Analytics is called “unique events”. It is not “number of video completions”. A calculated metric in GA cannot divide the value in row 7 by a value in row 1. In can only calculate values (add, subtract, multiply, divide) between columns. I also need to stress that custom metrics, since they can be used for a wide variety of purposes (including creating custom visit scoring!!), will not have any “uniques” applied to them, pageviews, content views, or events do. This is a pretty big limitation, though I don’t see it see it changing any time soon.

In any case, the ability to calculate metrics in Google Analytics has made the platform much more powerful, but a huge amount of that power now lies in properly implementing custom metrics.

Custom Metrics, Calculated Metrics, and Google Analytics Premium

The launch of calculated metrics creates another major differentiation between Google Analytics Standard and Google Analytics Premium. For a long time, I have believed that the number of custom dimensions available for GAP (200!) vs. GA Standard (20) was one of the most core selling points for larger companies who would be considering Premium. Then I began to fall in love with custom metrics and I’ve added that to the list too (again, 200 vs. 20). With calculated metrics, the GA Premium folks get a healthy 50 metrics to get them going, while the rest of world must settle with a meager 5. Yup, just 5. :-/

Closing Thoughts

Over the past 2 years or so, I have become a bigger and bigger fan of custom metrics. The release of calculated metrics in GA makes the possibilities of what the tool can do only more awesome. To be clear, the release of calculated metrics doesn’t change the final output that analysts such as myself or my colleagues would be providing to our clients. And I don’t expect tools like Excel to drop significantly in their utility just because GA launched calculated metrics. But I totally adore the direction Google Analytics is going in allowing for better and better analysis capabilities natively in the tool. To whatever extent GA ends up developing into a one ring to rule them all central BI hub, well… I, for one, welcome our analytics overlords.

The following post is an email that I wrote for the Traffic1M course presented by SumoMe.com. It is geared to folks who are just getting started with Google Analytics, and online marketing in general. A big thanks to Noah Kagan and team for the opportunity to participate and provide some content to a wide readership. If you are a reader of this blog and don’t know Noah, I highly recommend checking out okdork.com and sumome.com; there’s great stuff there.

Introduction to Google Analytics

If your website is like millions of other websites, then you have Google Analytics installed.

When I say “millions”, I’m referring to data from from BuiltWith.com that estimates almost 30 million (!) websites are using GA.

I also note that Google Analytics’ accounts are numbered sequentially, so as of the writing of this email more than 67 million accounts have been created. Not all are in use, so the number of active accounts is probably somewhere in between.

So you have GA on your site, and tons of data is being collected. What now? How can you use that data to help grow your business?

Why Use Google Analytics?

This Traffic1M course is all about getting that first million visitors to your site. Put simply, Google Analytics is the most popular tool you can use to measure those visitors. You shouldn’t use a tool because it is popular, though.

Most folks who choose GA do so because

It is really powerful (“enterprise-class”) software.

The Standard version is free.

Google’s tremendous cloud infrastructure allows them to rip through very large data sets rather quickly.

It has a nice, clean User Interface.

They want to use data to make informed business decisions.

That last point is the most important one. Let’s not be naive; data on it’s own is meaningless. But when used properly, data will help you see what’s working well, what’s not working, and help you make decisions about “what to do next.”

Where do I start?

One of the biggest challenges I have seen for businesses / website owners is that they don’t know where to begin when it comes to analytics. People find themselves looking at an imposing mountain of data, and don’t know how to begin climbing.

This post is authored by David Vallejo. At the current juncture, this blog is not configured to support for multiple authors. I hope to remedy that in the future. ~~Yehoshua

Since Google Tag Manager was released, here at Analytics Ninja we have faced a number of problems when migrating a “hard-coded” Google Analytics implementation.

The most common problem relates to actual moment of deployment, when the old Google Analytics code has to be removed from the site. This is not much of a problem if we have access to the site and we can directly remove the hard-coded snippet at the same time you publish your new container. But let’s be real, this is not the usual scenario. We usually rely on some other company or the client’s IT department to try to synchronize the deployment. This leaves us in a challenging situation because if they remove the code and we don’t publish the container right away, some data may get lost. Or, on the other hand, if they don’t remove the code and we publish the container the hits will be sent twice. Or even worse … if we are migrating from Classic to Universal at the same time we’re moving to Google Tag Manager we could end even messing up the sessions/users/bounce rates and any other metrics/dimensions.

This problem is aggravated if we are working on a multi-domain implementation where each of the domains is being ran by different business groups… if it was hard to synchronize with one team, just imagine if there are 2 or more business groups in different time zones and having everyone required to make changes at the same time …

Even if we are able to get everyone involved in the migration, there will always be some little time gap between the code removal and the container publication.

One solution is to use a piece of code that will allow us to block all our new tags if the old code is still on the site. We will just need to schedule a date for the migration and using a simple macro and rule we’ll be just firing our new tags on the pages that have the old code already removed. This way even if there is a long timeframe to get everything sorted out, the GA data won’t be affected at all.

For this to work, we need a macro to get the current Google Analytics status on the page. For this we’ll configure in our Macro the UA property names (we’re using an array because we may have a dual tracking implementation, or the page may have a third party GA tracking and we don’t want to mess up with them). Then we’ll loop through all the trackers available to get their configured UA account, and if it matches our properties arrays we’ll return true. See following flow diagram to see what’s the macro’s logic:

Google Tag Manager Migration Macro Flow Chart

The next step we need to take is to setup a new firing rule, that will allow us to block our tags if the old trackers are still on the pages.

Macro Code

We then add a blocking rule to our Google Tag Manager tags so they are not executed if any previous tracker initialized on the page.

We’ll need to keep one more thing in mind. As GA/UA code is asyncronous it may happen that Google Tag Manager tags get fired before the old code get executed, so we’ll need to delay our current tags while we’re migrating . This can be done setting the firing tag to DomReady event ( gtm.dom ), or even better for Window Load ( gtm.load ). This is not the best way to run an analytics implementation as we normally want our analytics tag to get fired ASAP, but we’ll be changing our tags firing rule to gtm.js/All Pages when we check the old code is already gone from the pages.

Let us know what you think about this implementation method in the comments section below.

A better way to measure content engagement with Google Analytics

This post is inspired by a conversation that I had with my friend and colleague Simo Ahava at Superweek as well as a recent work request from a well-established Italian publisher. In short, the publisher was quite challenged by the fact that they had an 85% bounce rate, and that their time on site was so low. Their articles tend to the get many hundreds, if not thousands, of Facebook likes, so “how could it be that users were spending so little time on site?!” Their average time on page was around the three minute mark, so how could be that average session duration was significantly lower?

Challenge 1: Google Analytics tracks time on page / on site by measuring difference between time stamps of hits. If the page is a bounce, no time will be recorded.

Challenge 2: Even if the page viewed is not the bounce/exit page (and thereby has a time greater than zero), GA doesn’t distinguish between time on page/site if the browser window is in a hidden or visible tab

After a lengthy explanation to the client informing them of the way the Google Analytics tracks time on page (and by extension, time on site), they were still stuck without a way to accurately measure content engagement. First of all, there are a number of different ways to measure engagement besides time on page / site. Many posts have been written about this and I urge readers to seek those out since time metrics gain too much undue focus as it is. As things stand, since this publisher’s site was not configured with any event tracking (a scroll tracking module would be great), they were seeing many users come to their site, view one page, and then leave. Unfortunately for them, “out of the box GA” does not provide very good insights into the nature of how users are interacting with their content. “Are they even reading the content?”Continue reading REAL Time On Page in Google Analytics

From Data Layer to Dollars…

Some visitors are more profitable than others, and thoughtfully created Remarketing Lists can help businesses focus their ad spend on the most valuable visitors. This can improve revenue, reduce costs, or both.

The key is discovering the common characteristics of visitors which make them more valuable than an average visitor, and then preferentiallydelivering ads (i.e. bid higher) to users who have these characteristics. In other words, if you can segment your visitor base to identify which users have a higher potential value, you’ll be able to make smarter decisions with your advertising budget. Utilizing features of Google Analytics and Google Tag Manager provides the opportunity to do this.

One of my favorite features of the Google Analytics / DoubleClick integration is the ability to add users to Adwords Retargeting lists with the click of a button. Here’s an example of how I might come up with a good remarketing list:

Let’s start with a curious question –> How long does it take users to convert on the site? The first place I would go to begin answering this question is by applying a “converted” segment (in this case, a purchase) to the Session Duration report.

Right away I notice that it takes a large percentage of users over 10 minutes in order to make a purchase, and over almost 13% require a half an hour or more. While I very much like segmenting the Engagement Reports, in this particular case I’m going to look at the User Timings report as I believe the data visualization is more helpful there (you can expand the histogram).
Continue reading Advanced Remarketing with Google Analytics & Google Tag Manager

Universal Analytics

The big news last week (at least for folks like me) was that Universal Analytics finally came out of beta. Is it time for you to switch?

Short answer –> yes, soon. 🙂

What exactly is the big deal about Universal Analytics? My current take on the product’s features is what follows:

UserID

One of the most touted feature improvements over Google Analytics Classic is the introduction of UserID. Google lists 4 benefits of using UserID.

More accurate user count

Analyze the signed-in experience

Access the User ID View and Cross Device Reports

Connect your acquistions, engagement, and conversions.

While I see the move towards a most person / customer centric view by the GA team to be a big step in the right direction, I think that at the current juncture the UserID reporting (and data model) falls flat. Full disclosure: I’ve only had access to the UserID reports for a few days. AND I am acutely aware that the GA team is constantly innovating and improving their product at a dizzying pace. That means that the only thing I can truly count on when it comes to GA is that the product will continue to improve (and hopefully not make this blog post completely irrelevant in the next 3 days).

So, why does UserID currently fall flat? Doesn’t the ability to connect all the dots sound like a marketer’s dream?

The following is the tale of some investigative work I did for a company which approached me to help them with their Google Analytics tracking for their online store. This company, like so many others, sells items on the Internet and wants to be able to properly attribute their sales to the correct channel using Google Analytics. Not surprisingly, in addition to having subdomains they were using a third party shopping cart and needed to have cross domain tracking configured. Pretty simple. Or so I thought…

As it turns out, the third party shopping cart they were using is called Shopify. As far as being an intuitive user interface that makes it easy for non-technical users to set up their store, I certainly see a lot of positives with Shopify. Unfortunately, Shopify also tried to make setting up Google Analytics “dumb proof.” In the end, trying to figure out why I couldn’t properly set up a simple, working basic GA tag led me to be “dumbfounded.” Shopify has a very simple interface where one simply needs to copy and paste their Google Analytics tracking code in order to get started.

However, as soon as you save the file, Shopify takes the Google Analytics code and rewrites it to match their own settings. In particular, they are choosing to _setDomainName to “none”, adding _setAllowLinker (which would indeed be required for cross domain tracking) and switched to dc.js. (I am not sure if the folks at Shopify paid attention or not, but to use the DoubleClick cookie, users are supposed to Continue reading Shopify Google Analytics Integration

A question that is commonly asked of analysts is “WHERE DID MY TRAFFIC GO?!” (Yes, even occasionally emailed in all caps). 🙂 Indeed, this is a question that I received from a publisher recently, though they were very polite and didn’t use all caps. For publishers especially, this question is directly tied to their bottom line as advertising revenue is linked directly to pageviews (CPM models etc). So, I rolled up my sleeves and got ready to do a bit of analysis to see where their traffic went.

The following is simply a recounting of a bit of my process. The purpose of this post is to share some of my methods with a target audience of beginner to intermediate level analysts. This is a “how to” oriented post; nothing particularly new or groundbreaking here. Just some good old fashioned analysis of a common client question.

Leveraging Custom Dimensions and Custom Metrics to gain insights into Merchandise and Profitability.

I spend a bit too much time on Twitter. It’s not a terrible thing, as in addition to Twitter being a forum that truly keeps me informed about what is happening in my industry allowing me to stay on the cutting edge for my clients, it is also a social outlet that helps keep me from completely getting swallowed by work. 🙂 That said, sometimes it is hard to wade one’s way through all of the chatter in order to find the good stuff. One of the people out there who is almost always tweeting quality things is Kevin Hillstrom, @minethatdata. It just so happens that yesterday seemed to be a minor “@minethatdata appreciation day” with some other industry peeps giving Kevin a well-deserved thumbs up.

One of the things that Kevin consistently wants others in the digital measurement industry to think about is merchandise and profit. A simple search on his timeline for July 2013 shows that he mentioned profit no less than 43 times and merchandise at least 28 times.